Article Per Year (5 Year)
p-Index From 2021 - 2026
4.454
P-Index
This Author published in this journals
All Journal Jurnal Maneksi (Management Ekonomi Dan Akuntansi) Journal of Economic, Bussines and Accounting (COSTING) Primanomics : Jurnal Ekonomi & Bisnis Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Jurnal Ilmiah Akuntansi dan Finansial Indonesia JIMEK : Jurnal Ilmiah Mahasiswa Ekonomi Jurnal Ilmiah Akuntansi dan Bisnis Akurasi : Jurnal Studi Akuntansi dan Keuangan Jurnal Bina Akuntansi Jurnal Bisnis dan Kewirausahaan (Journal of Business and Entrepreneurship) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Indonesian Journal of Society Engagement (IJSE) Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) PEDAGOGI: Jurnal Ilmiah Pendidikan Jurnal Abdi Mandala Jurnal Ilmu Manajemen & Ekonomika Jurnal Manajemen, Akuntansi, Ekonomi Jurnal Dinamika Akuntansi dan Bisnis (JDAB) Media Ilmiah Akuntansi Paradoks : Jurnal Ilmu Ekonomi INQUISITIVE: International Journal of Eonomic Kajian Akuntansi Jurnal Akuntansi Atestasi : Jurnal Ilmiah Akuntansi
Randy Kuswanto
Accounting Program, Wiyatamandala School Of Business, Indonesia
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Kajian Akuntansi

 1 
KEY AUDIT MATTERS IN PRACTICE: AN EMPIRICAL STUDY OF AUDITOR REPORTING IN INDONESIA Angelita, Angelita; Randy Kuswanto
Kajian Akuntansi Vol. 26 No. 1 (2025): June 2025
Publisher : UPT Publikasi Ilmiah UNISBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/kajian_akuntansi.v26i1.7178

Abstract

This study aims to explore and analyze the practice of KAM disclosure by auditors in Indonesia and identify whether there are certain patterns or trends in KAM disclosure based on the characteristics of the company, auditor, or industry concerned. This study uses descriptive analysis method with a total sample of 1,664 observations. The main source of information of this study is secondary data obtained from audit reports that have been published by IDX listed companies in 2022 and 2023. The test results prove: First, the main topics of auditor concern remain consistent, namely revenue recognition, receivables, and fixed assets. Second, there is an increase in standardization towards more transparent audit reporting. Third, there is an observable pattern in the distribution of auditor. This research data still does not explore more deeply how auditors disclose KAM, so further research is needed. Efforts that can be made include in-depth interviews with auditors at various KAPs, analyzing KAM disclosure based on KAP size, or assessing more deeply the effectiveness of KAM disclosure policies and their impact on overall audit quality.
POTENTIAL STOCK PRICE TREND PREDICTION USING GENERATIVE AI MODEL: (COMPARATIVE STUDY BASED ON FINANCIAL RATIO DATA AND HISTORICAL STOCK PRICES) Radjah, Lidia; Kuswanto, Randy
Kajian Akuntansi Vol. 26 No. 1 (2025): June 2025
Publisher : UPT Publikasi Ilmiah UNISBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/kajian_akuntansi.v26i1.7190

Abstract

The rapid advancement of generative AI offers notable implications for investment decision-making, yet studies utilizing financial ratios to predict stock prices remain limited. This research aims to evaluate the potential of AI models ChatGPT, Gemini, Deepseek, and Claude in forecasting LQ45 stock price trends using financial ratios and historical data, while also testing the consistency of their predictions over time. Employing an experimental quantitative approach, this study analyzes predictions made by four AI models for 23 LQ45-listed companies during the 2021–2023 period. Robustness was assessed by administering identical prompts at two different times and analyzing the results using the Paired Sample t-Test. Accuracy was evaluated at two levels: trend prediction accuracy (Level 1) and price prediction error (Level 2). The findings reveal that while AI models show relatively stable performance in trend direction prediction, their accuracy varies across models. Forecasting exact stock prices remains challenging, indicating AI's current limitations as a fully reliable predictive tool.
Co-Authors Adirianto Adrian Muluk, Muhammad Aliani, Deva Fitri Anabel, Belicia Angelita Angelita, Angelita Antonio Chandra, Ricky Breliastiti, Ririn Budi Kurniawan Defina Dionosius, Lius Eka Putra Jaya Halimah, Elsa Nur Henry, Juphiter Indah Masri Izabel, Thentyana lindah, lindah Lius Dionosius Lukman, Hendro Luzezyta, Natasya Marundha, Amor Marvin Hendrily Ni Putu Eka Widiastuti Nuryati, Tutty Radjah, Lidia Riska Amelia Saprudin Saprudin, Saprudin Sellyna, Sellyna Sembiring, Rinawati Slamet Wahyudi Steven Andrianus Susilawati, Susilawati Tambunan, Indah Meilani Pramesti Tannama, Setyo Temy Setiawan, Temy Winardi, Juan Christian Wiwi Idawati Yosua